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Ines Wilms

Personal Details

First Name:Ines
Middle Name:
Last Name:Wilms
Suffix:
RePEc Short-ID:pwi441
[This author has chosen not to make the email address public]
https://www.maastrichtuniversity.nl/i.wilms

Affiliation

Vakgroep Kwantitatieve Economie
School of Business and Economics
Maastricht University

Maastricht, Netherlands
http://www.maastrichtuniversity.nl/web/Faculties/SBE/Theme/Departments/QuantitativeEconomics.htm
RePEc:edi:dqmaanl (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Marie-Christine Duker & David S. Matteson & Ruey S. Tsay & Ines Wilms, 2024. "Vector AutoRegressive Moving Average Models: A Review," Papers 2406.19702, arXiv.org.
  2. Alain Hecq & Ivan Ricardo & Ines Wilms, 2024. "Reduced-Rank Matrix Autoregressive Models: A Medium $N$ Approach," Papers 2407.07973, arXiv.org.
  3. Jeroen Rombouts & Marie Ternes & Ines Wilms, 2024. "Cross-Temporal Forecast Reconciliation at Digital Platforms with Machine Learning," Papers 2402.09033, arXiv.org, revised May 2024.
  4. Enrico Wegner & Lenard Lieb & Stephan Smeekes & Ines Wilms, 2024. "Transmission Channel Analysis in Dynamic Models," Papers 2405.18987, arXiv.org, revised May 2025.
  5. Yu Jeffrey Hu & Jeroen Rombouts & Ines Wilms, 2023. "Fast Forecasting of Unstable Data Streams for On-Demand Service Platforms," Papers 2303.01887, arXiv.org, revised May 2024.
  6. Robert Adamek & Stephan Smeekes & Ines Wilms, 2023. "Sparse High-Dimensional Vector Autoregressive Bootstrap," Papers 2302.01233, arXiv.org, revised May 2025.
  7. Alain Hecq & Marie Ternes & Ines Wilms, 2023. "Hierarchical Regularizers for Reverse Unrestricted Mixed Data Sampling Regressions," Papers 2301.10592, arXiv.org, revised Nov 2024.
  8. Luca Barbaglia & Christophe Croux & Ines Wilms, 2022. "Detecting Anti-dumping Circumvention: A Network Approach," Papers 2207.05394, arXiv.org.
  9. Robert Adamek & Stephan Smeekes & Ines Wilms, 2022. "Local Projection Inference in High Dimensions," Papers 2209.03218, arXiv.org, revised Apr 2024.
  10. Alain Hecq & Marie Ternes & Ines Wilms, 2021. "Hierarchical Regularizers for Mixed-Frequency Vector Autoregressions," Papers 2102.11780, arXiv.org, revised Mar 2022.
  11. Ines Wilms & Jacob Bien, 2021. "Tree-based Node Aggregation in Sparse Graphical Models," Papers 2101.12503, arXiv.org.
  12. Robert Adamek & Stephan Smeekes & Ines Wilms, 2020. "Lasso Inference for High-Dimensional Time Series," Papers 2007.10952, arXiv.org, revised Sep 2022.
  13. Stephan Smeekes & Ines Wilms, 2020. "bootUR: An R Package for Bootstrap Unit Root Tests," Papers 2007.12249, arXiv.org, revised Jul 2022.
  14. Vanessa Berenguer Rico & Ines Wilms, 2018. "White heteroscedasticty testing after outlier removal," Economics Series Working Papers 853, University of Oxford, Department of Economics.
  15. Luca Barbaglia & Christophe Croux & Ines Wilms, 2017. "Volatility Spillovers and Heavy Tails: A Large t-Vector AutoRegressive Approach," Papers 1708.02073, arXiv.org.
  16. Stéphanie Aerts & Ines Wilms, 2017. "Cellwise robust regularized discriminant analysis," Working Papers of Department of Decision Sciences and Information Management, Leuven 563648, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven.
  17. Ines Wilms & Luca Barbaglia & Christophe Croux, 2016. "Multi-class vector autoregressive models for multi-store sales data," Working Papers of Department of Decision Sciences and Information Management, Leuven 540947, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven.
  18. Ines Wilms & Jeroen Rombouts & Christophe Croux, 2016. "Lasso-based forecast combinations for forecasting realized variances," Working Papers of Department of Decision Sciences and Information Management, Leuven 553087, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven.
  19. Luca Barbaglia & Ines Wilms & Christophe Croux, 2016. "Commodity Dynamics: A Sparse Multi-class Approach," Papers 1604.01224, arXiv.org, revised Oct 2016.
  20. Ines Wilms & Christophe Croux, 2015. "An algorithm for the multivariate group lasso with covariance estimation," Working Papers of Department of Decision Sciences and Information Management, Leuven 516983, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven.
  21. Ines Wilms & Sarah Gelper & Christophe Croux, 2015. "The predictive power of the business and bank sentiment of firms: A high-dimensional Granger Causality approach," Working Papers of Department of Decision Sciences and Information Management, Leuven 504661, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven.
  22. Ines Wilms & Christophe Croux, 2014. "Robust sparse canonical correlation analysis," Working Papers of Department of Decision Sciences and Information Management, Leuven 472948, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven.

Articles

  1. Robert Adamek & Stephan Smeekes & Ines Wilms, 2024. "Local projection inference in high dimensions," The Econometrics Journal, Royal Economic Society, vol. 27(3), pages 323-342.
  2. Ines Wilms & Sumanta Basu & Jacob Bien & David S. Matteson, 2023. "Sparse Identification and Estimation of Large-Scale Vector AutoRegressive Moving Averages," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(541), pages 571-582, January.
  3. Adamek, Robert & Smeekes, Stephan & Wilms, Ines, 2023. "Lasso inference for high-dimensional time series," Journal of Econometrics, Elsevier, vol. 235(2), pages 1114-1143.
  4. Bottmer, Lea & Croux, Christophe & Wilms, Ines, 2022. "Sparse regression for large data sets with outliers," European Journal of Operational Research, Elsevier, vol. 297(2), pages 782-794.
  5. Wilms, Ines & Rombouts, Jeroen & Croux, Christophe, 2021. "Multivariate volatility forecasts for stock market indices," International Journal of Forecasting, Elsevier, vol. 37(2), pages 484-499.
  6. Vanessa Berenguer-Rico & Ines Wilms, 2021. "Heteroscedasticity testing after outlier removal," Econometric Reviews, Taylor & Francis Journals, vol. 40(1), pages 51-85, January.
  7. Barbaglia, Luca & Croux, Christophe & Wilms, Ines, 2020. "Volatility spillovers in commodity markets: A large t-vector autoregressive approach," Energy Economics, Elsevier, vol. 85(C).
  8. I. Wilms & C. Croux, 2018. "An algorithm for the multivariate group lasso with covariance estimation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(4), pages 668-681, March.
  9. Ines Wilms & Luca Barbaglia & Christophe Croux, 2018. "Multiclass vector auto‐regressive models for multistore sales data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 67(2), pages 435-452, February.
  10. Wilms, Ines & Gelper, Sarah & Croux, Christophe, 2016. "The predictive power of the business and bank sentiment of firms: A high-dimensional Granger Causality approach," European Journal of Operational Research, Elsevier, vol. 254(1), pages 138-147.
  11. Wilms, Ines & Croux, Christophe, 2016. "Forecasting using sparse cointegration," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1256-1267.
  12. Christophe Croux & Ines Wilms, 2016. "Discussion of ‘Asymptotic Theory of Outlier Detection Algorithms for Linear Time Series Regression Models’," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 353-356, June.
  13. Barbaglia, Luca & Wilms, Ines & Croux, Christophe, 2016. "Commodity dynamics: A sparse multi-class approach," Energy Economics, Elsevier, vol. 60(C), pages 62-72.
  14. Gelper, Sarah & Wilms, Ines & Croux, Christophe, 2016. "Identifying Demand Effects in a Large Network of Product Categories," Journal of Retailing, Elsevier, vol. 92(1), pages 25-39.

More information

Research fields, statistics, top rankings, if available.

Statistics

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Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 20 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (14) 2016-07-02 2016-11-06 2017-01-15 2017-08-13 2018-06-25 2020-08-24 2021-02-15 2021-03-01 2022-10-17 2023-02-27 2023-03-13 2024-07-15 2024-08-12 2024-09-02. Author is listed
  2. NEP-ETS: Econometric Time Series (11) 2016-07-02 2017-08-13 2017-09-10 2020-08-24 2020-08-24 2021-03-01 2022-10-17 2023-02-27 2023-03-13 2024-08-12 2024-09-02. Author is listed
  3. NEP-RMG: Risk Management (3) 2016-11-06 2017-08-13 2017-09-10
  4. NEP-BIG: Big Data (2) 2021-02-15 2024-03-25
  5. NEP-FOR: Forecasting (2) 2016-11-06 2024-03-25
  6. NEP-NET: Network Economics (2) 2021-02-15 2022-08-29
  7. NEP-ORE: Operations Research (2) 2018-06-25 2020-08-24
  8. NEP-PAY: Payment Systems and Financial Technology (2) 2023-04-03 2024-03-25
  9. NEP-AGR: Agricultural Economics (1) 2016-04-16
  10. NEP-CBA: Central Banking (1) 2024-07-15
  11. NEP-CMP: Computational Economics (1) 2024-03-25
  12. NEP-COM: Industrial Competition (1) 2016-07-02
  13. NEP-ENE: Energy Economics (1) 2017-09-10
  14. NEP-INT: International Trade (1) 2022-08-29
  15. NEP-MAC: Macroeconomics (1) 2021-03-01

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